Transforming Data Governance: How Data Ideology Helped a US Steel Manufacturer Reduce Compliance Risks and Drive Value - Data Ideology

CASE STUDY

Transforming Data Governance to Reduce Compliance Risks and Drive Value

Steel

Enterprise

Data Governance

KEY OUTCOME

A leading US-based steel manufacturer embarked on a strategic initiative to implement a Data Governance (DG) framework with a focus on sustainability, specifically aimed at improving data quality, security, and governance for sustainability reporting, particularly carbon emissions.

How did Data Ideology solve this organization’s challenges?

CHALLENGE SUMMARY

The organization suffered from a lack of data accountability, the risk of non-compliance during audits, and concerns over the integrity and accuracy of sustainability-related data. Unclear data ownership, audit risk due to the lack of a governance framework, issues with data integrity and accuracy for sustainability metrics, and inadequate data management practices with pervasive data silos were consistent major issues.

SOLUTION SUMMARY

The organization adopted a tailored, incremental approach to data governance, allowing them to build a framework aligned with their unique needs and culture. A robust governance structure with clearly defined roles and responsibilities ensured effective management of governance processes. By prioritizing sustainability data, they not only met regulatory requirements but also established a strong foundation for future growth and data-driven decision-making across the business.

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Unpacking the Insights: A Deeper Dive into Our Approach

THE CHALLENGE

A organized faced several key challenges:

  1. Lack of Accountability: There was no clear ownership of data, complicating efforts to meet regulatory reporting requirements.
  2. Audit Risk: Without a structured governance framework, the organization was exposed to the risk of non-compliance with government audits.
  3. Data Integrity and Accuracy: Issues with data reliability were especially problematic for sustainability metrics critical to the organization’s reporting needs.
  4. Data Management: Proper data management practices did not exist in the organization with data silos existing throughout the sustainability data flow.

DATA IDEOLOGY’S SOLUTION

The organization and Data Ideology adopted a multi-phase, customized data governance framework, focusing on the following:

  1. Guiding Principles: The data governance framework was based on a series of guiding principles such as integrity, transparency, auditability, stewardship, and accountability. These principles formed the foundation of the governance initiatives.
  2. Data Governance Structure: The governance structure consisted of two governing bodies in the initial phase—a Data Governance Working Group and a Stewardship Committee. The Working Group was responsible for operationalizing the framework and its policies, while the Stewardship Committee was tasked with overseeing data definitions and maintaining custodianship over data within their respective domains.
  3. Core and Supporting Disciplines: We focused on managing and integrating data from various sources to create a unified, accurate view of the organization’s data, while prioritizing the protection of sensitive information from unauthorized access or breaches. By implementing effective data lifecycle management, we ensured that data was efficiently handled from creation to obsolescence, reducing risks and improving productivity. Additionally, we enabled business leaders to access timely, accurate insights through advanced analytics and reporting, facilitating better decision-making.
  4. Technology Enablement: The organization implemented various foundational tools to support data governance, including a data hub for carbon emission management, leveraging Microsoft SharePoint for storing business glossaries, and using Microsoft Excel for the initial versioning process.

THE RESULTS

By adopting a phased approach, the organization gradually developed and matured its Data Governance framework, with a primary focus on sustainability:

  1. Improved Data Integration and Quality: The creation of a centralized business glossary and a data hub for carbon emissions helped standardize data across the organization, improving its quality and accessibility for sustainability reporting.
  2. Clear Accountability and Oversight: The formation of dedicated working groups and committees established clear accountability for data governance, ensuring that data management processes were transparent and auditable.
  3. Reduced Compliance Risks: The new data governance framework significantly reduced the risk of audit failures by implementing well-documented processes and a structured issue resolution mechanism, ensuring compliance with sustainability reporting standards.

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